Perform a full text search with the given query

Build the fuzzy simplicial set from kNN data. This computes bandwidths, membership strengths, and applies the fuzzy set union operation to produce a

Generate MNIST benchmark datasets.

Generate NNDescent benchmark datasets.

Generate a single dataset with ground truth.

Import the given list of modules.

Pick a nice time step that divides the given duration into approximately desiredCount bins.

Fill remaining empty neighbor slots with random points.

Initialize the heap from RP tree leaf co-occurrences. For each leaf, compute distances between all pairs and insert into heap. Processes leaves in bat

GPU-accelerated RP tree initialization. Collects all pairwise indices from leaf co-occurrences, dispatches them to the GPU for distance computation,

* Spread per-window `scores` across every character of a `length`-char text by * linear interpolation between window centers, emitting one span per c

Consume the optimizer and return the embedding.

Whether the metric uses angular (rather than euclidean) RP tree splitting

* L2-normalize each `dim`-length row of `data` in place, so a dot product * between two rows is their cosine similarity. Zero-norm rows are left as-i

Converts a list of point sample weights to linear sample locations and weights. * The idea is that with linear texture sampling, we can sample two pi